Private AI: taking back control of your data

Data sovereignty has become the primary barrier to AI adoption in business. Executives want the benefits of large language models, but refuse to expose their strategic data to third parties — whether US cloud providers or offshore vendors.

This concern is legitimate. Data leak incidents through SaaS AI tools — sometimes unintentional, linked to providers' training policies — have put CISOs and legal departments on alert. GDPR adds a further layer of complexity around cross-border data transfers.

The technical answer is private AI, or on-premise AI. It consists of deploying language models directly within your infrastructure — either on your physical servers or in your private cloud (isolated VPC) — so that your data never leaves your security perimeter.

Recent advances make this approach economically viable for mid-market companies. Models such as Llama 4, Mistral Medium or Microsoft PHI-4 offer performance comparable to large cloud models on most enterprise use cases, at a controlled infrastructure cost. A mid-range GPU server is sufficient for many applications.

Deploying a private LLM follows four steps: choosing the model suited to the use case, optional fine-tuning on your business data, integration with your existing systems (ERP, CRM, databases), and establishing access governance and query auditing.

Compliance by design is an additional advantage. By keeping data within your perimeter, you drastically simplify your Data Protection Impact Assessment (DPIA) and reduce AI Act risks for high-risk systems.

Private AI is not reserved for large enterprises. With the right architecture and models, a company of 200 people can deploy a sovereign, performant and compliant AI solution, operational in under three months. This is what we build with our clients.